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LLMs & Generative AI for Practitioners

Intermediate

The applied LLM course. Skip the math; learn what you actually need to ship LLM-powered features. Tokens, prompts, RAG, fine-tuning, evaluation, guardrails, and cost optimization — for engineers and data scientists who already know ML. Six hands-on cases mirror real production builds. Covers Claude, GPT, and open models (Llama, Mistral) in 2026. Builds on ML Foundations and ML in Practice.

LLMs & Generative AI for Practitioners

Duration

4 weeks

Content

4 modules, 32 lessons

Students

2

Total Time

~7 hours

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Course Curriculum

1

LLM Foundations: What They Actually AreFREE PREVIEW

What LLMs are (and aren't); tokens, embeddings, and the transformer in plain language; the pretraining/fine-tuning/prompting spectrum; the 2026 model landscape (closed vs open, sizes, costs); context windows.

85 min8 lessons

What LLMs Are (and What They Aren't)

Recap — Tokens, Embeddings, and the Transformer in Plain Terms

The Pretraining vs Fine-tuning vs Prompting Spectrum

Recap — Picking the Right Adaptation Strategy

How to Compare Models — Closed vs Open, Size, Cost

Recap — The 2026 Model Landscape and How to Choose

Tokens, Context Windows, and Why They Matter

Recap — Working Within Token Budgets

2

Prompt Engineering and PatternsFREE PREVIEW

Prompt anatomy; few-shot, chain-of-thought, structured output; tool use and function calling; prompt caching and cost optimization; the anti-patterns that waste tokens and produce flaky outputs.

110 min10 lessons

Prompt Engineering Fundamentals

Recap — The Prompt Anatomy

Few-Shot, Chain-of-Thought, and Structured Output

Recap — When Each Pattern Wins

Tool Use and Function Calling

Recap — Designing Tool-Using Agents

Prompt Caching and Cost Optimization

Recap — Caching for Production Apps

Common Prompt Engineering Anti-Patterns

Recap — What Not to Do

3

RAG, Fine-tuning, and EvaluationFREE PREVIEW

Retrieval-augmented generation done properly; embeddings and vector databases; when fine-tuning is worth it (and when it isn't); evaluating LLM outputs in ways that actually catch bugs.

90 min8 lessons

Retrieval-Augmented Generation (RAG) Foundations

Recap — Building a Working RAG Pipeline

Embeddings and Vector Databases

Recap — Choosing an Embedding Model and Vector Store

When to Fine-tune (and When Not To)

Recap — Fine-tuning Patterns and Costs

Evaluating LLM Outputs — The Hard Problem

Recap — LLM Eval That Actually Catches Bugs

4

Live LLM CasesFREE PREVIEW

Six end-to-end walkthroughs: production-ready RAG chatbot, tool-using agent, model comparison and evaluation, token cost optimization for high-volume apps, hallucination guardrails, and fine-tuning a small model for domain specialization.

150 min6 lessons

Case 1 — Build a Production-Ready RAG Chatbot

Case 2 — Add Tool Use to an LLM Agent

Case 3 — Evaluate Two LLMs for a Specific Task

Case 4 — Optimize Token Costs for a High-Volume App

Case 5 — Diagnose Hallucinations and Add Guardrails

Case 6 — Fine-tune a Small Model for Domain Specialization

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